In their interesting article (1), Dr. Richardson and colleagues reported hypertension, cardiovascular disease, obesity, and diabetes mellitus, as frequent comorbidities in patients with COVID-19, consistent with previous studies (2, 3). A common underlying feature of all these disorders is endothelial dysfunction. Furthermore, all of the co-factors bound by SARS-CoV-2 coronavirus to access host cells, including ACE2, CD147, sialic acid receptor, and TMPRSS2, are expressed by human endothelial cells (4). Hence, we hypothesize that SARS-CoV-2 could directly affect endothelial cells (4), explaining all systemic manifestations observed in COVID-19 patients, including thrombotic complications (2, 5). If our theory is correct, drugs that have been shown to ameliorate endothelial function, such as modulators of the renin-angiotensin-aldosterone system, anti-coagulants, statins, and anti-inflammatory drugs, could be helpful in COVID-19 patients.

The authors show in the supplementary tables some data regarding the percentage of patients who were receiving ACE inhibitors and angiotensin II receptor blocker. Can they perform a multivariate analysis to verify whether such treatment(s) had any effect on the severity of disease? Equally important, the authors show an increased average value of D-dimer in their population: do they have any data on actual coagulation disorders?

Richardson et al (1) bring numbers from the American epicenter and important data regarding known male gender vulnerability (2): the drastic difference of over 6 times more male fatalities in a very productive age range (40-49 years) and approximately two times more male admissions from 30-49 years.

One biologic explanation for this vulnerability is that Transmembrane Protease, Serine 2 (TMPRSS2), the protease that primes the Spike protein of the virus for infectivity, is an androgen-expressed protein (3). TMPRSS2 is expressed in the adult lungs (4). Both TMPRSS2 and Angiotensin Converting Enzyme 2 (ACE2), the virus receptor, are expressed in male and female lungs (5). Interestingly, the genes of ACE2 and the Androgen Receptor are located on the X chromosome (3).

In a preliminary study in 41 Spanish men admitted due to severe COVID-19, mean age was 58 years (range 23-79) and 71% had androgenetic alopecia (7).

Male vulnerability to SARS-CoV-1 has well studied in murine experiments (8). Male's lungs presented more viral load/damage. Females presented resistance and ovariectomy increased their vulnerability. Prophylaxis with the anti-androgen agent flutamide improved survival: 20% of the treated vs 0% of the control group among males (8). Remdesivir failed to demonstrate benefit in experimental male mice survival (9). Anti-androgen therapy remains to be tested in humans as a possible therapy to modify this well-known host risk factor.

In this interesting paper, Richardson et Al report a very high ICU mortality rate.

In one month, 373 cases were being treated in the ICU (14%). A total of 320 patients received mechanical ventilation and 282 died, with a mortality of 88,1%. The overall death rate for COVID-19 for patients admitted to critical care is much higher than for other viral pneumonias (about 22.2%).

Which scenarios can be considered when interpreting deaths from COVID-19?

Our brief experience in the UAE is almost the same. In addition, we observed very high plasma glucose even among previously non-diabetic patients with/without acidosis. Maybe this virus affects the pancreas and blocks insulin secretion.

Secondly, we observed that despite the clinical and radiologic picture of COVID-19, many patients had negative nasopharyngeal swab, even when done 3 times.

Thirdly, many patients responded to hydroxychloroquine (we observed).

CONFLICT OF INTEREST:None Reported

April 23, 2020

Very Misleading

John Yuill, MD
| Hospitalist, Community Hospital, TN

Several media outlets have reported the 88% mortality rate without explaining that the many patients who remained hospitalized at the time the study ended are excluded, and that therefore it is too soon to determine final mortality rate.

See https://twitter.com/DrJohnScott/status/1253284899752009728 for a cogent explanation of the bias in this study.

With all the emphasis on the availability of ventilators in the current pandemic it is important to remember that ventilators are not self-driving machines. Qualified personnel are required to operate them. Injuries can derive from the improper use of the devices. In fact, each patient may require different ventilation strategies depending on the presence of coexisting lung disease on which ARDS is superimposed. Therefore all recommendations offered may not apply to an individual patient. Even in presence of ARDS without underlying lung disease injury can occur and strategies should be implemented to avoid additional damage.

For example, PEEP traditionally has been used to improve oxygenation while reducing Fi02 . When trying to minimize iatrogenic complications of mechanical ventilation PEEP is a modality that can potentially minimize the injury caused by ventilation at low lung volumes by recruiting poorly ventilated lung units and keeping them open. The critical issue is how to define the ideal PEEP level in an individual patient without causing damage to other lung units. This can be monitored by CT scan or oxygenation response, both imperfect methods. PEEP levels often are estimated on the basis of a PEEP / Fi02. Higher PEEP levels have been
associated with decreased mortality in ARDS patients with Pa02 / Fi02 < 200mm Hg but not in patients with higher Pa02 / Fi02 ratios. PEEP level can also be guided by esophageal pressure with the goal of keeping positive P1 at end expiration positive, resulting in increase PO2 levels and respiratory compliance . However to my last check most units were not equipped with esophageal pressure monitoring device. Combination of plateau pressure and PEEP can provide guidance for choosing optimal lung protective ventilation strategy.

APRV modality has shown advantages in patients with ARDS but I am not sure how often it is used in this settings

Even the use of neuromuscular blockers in the management of ventilated COVID19 patients is controversial

These comments are only a partial list of the complexities facing those managing patients with ARDS on mechanical ventilation

In this article, Dr. Richardson and colleagues report:"Mortality for those requiring mechanical ventilation was 88.1%." This is based on 282 deaths at the time of censoring out of 320 total "discharged alive and dead" patients receiving invasive mechanical ventilation. However, a closer examination of table 5, row 1 reveals that 1151 total patients received invasive mechanical ventilation in the cohort over the study period. 831 of these patients (72%) remain alive in the hospital. This is not openly discussed in the article, aside from a brief allusion to the problem at the end of the limitations section. Unfortunately, the 88.1% mortality has already gained widespread attention in the press and appears to have misled many to believe that this must represent the "true" mortality rate of ventilated patients. This conclusion cannot be drawn from these data.

Recently Zhang et al (1) reported a cohort of 1100 hospitalized COVID-19 patients who were hypertensive. Patients receiving inpatient ACEi or ARB treatment had far lower mortality in both unadjusted (3.7 vs 9.8%) or adjusted models (HR 0.42, CI 95% 0.19-0.92). These data seem to contradict that observation, but paying attention to eTable 2, it can be deducted that figures shown in the text refer to home medication.

In this paper's patient cohort, there was an important switch between home and inpatient treatment (only half patients previously on ACEi/ARBs continued them during hospitalization and, according to data in the text and eTable 2, 107 out of 953 patients not taking ACEi or ARBs previously were switched to them during hospitalization). So, as home treatment was poorly related to inpatient treatment, it would be interesting to show supplementary data of mortality according to ACEi/ARBs usage during hospitalization and verify if they are consistent with a reduction in mortality.

There is a substantial error in the estimation of mortality among patients requiring mechanical ventilation which I fear has led to a biased (overly high) estimate. This is the estimate (88%) that has been cited so often in the media.

Based on the data presented in Table 5 of the manuscript, 1,151 patients hospitalized for COVID-19 required mechanical ventilation (top row of table). Among this group 282 died while hospitalized, 38 were discharged from hospital, and 831 remained alive and hospitalized at the time that data were administratively censored as of April 4th, 2020. The authors estimated the cumulative incidence of mortality as 282 deaths divided by a denominator of 320 (the number of persons who either died or were discharged), excluding the remaining 820 hospitalized patients.

This approach to estimating cumulative mortality grossly distorts the actual death rate in mechanically ventilated patients. The Kaplan-Meier estimator, or other method for estimating censored survival, would provide a more accurate estimate of cumulative incidence of mortality, which is almost certainly much lower than 88%. As the authors’ estimate of 88% mortality among mechanically ventilated patients with COVID-19 has been widely reported in the lay press already, we are concerned that it will influence clinical decision making. Although we appreciate the enormous challenges in conducting large-scale epidemiologic research in the setting of an ongoing national health-care crisis and pandemic, it is important that clinical decision making be informed by accurate and non-biased estimates of outcomes.

Methodologic flaws that lead to overestimates of mortality from COVID-19

David Ost, MD
| MD Anderson Cancer Center

I read the report by Richardson and colleagues on the outcomes of COVID-19 patients in NewYork (1). The authors report that 88.1% of patients requiring mechanical ventilation died. This is potentially misleading and subject to misinterpretation.

The study enrolled 5,700 patients hospitalized with COVID-19 during a 35-day period from March 1 to April 4, 2020. Outcome analysis on the 2,634 patients who died or were discharged was performed. The remaining 3,066 patients that were alive and remain in the hospitals were excluded from analysis. Mechanical ventilation was required in 320 (12.1%) of the 2,634 patients in the analysis, and death occurred in 282 (88.1%) of these patients. Among the 3,066 patients still alive, 831 (27.1%) required mechanical ventilation but, because they had not been discharged, they were excluded from the analysis.

Mechanical ventilation is a marker of severity of illness and many of the patients on mechanical ventilation probably had acute respiratory distress syndrome (ARDS). ARDS carries a mortality of approximately 40%, with median time to death being 7 to 10 days (2, 3). Recovery is slow, with median hospital length of stay amongst survivors being 21 days for women and 25 days for men (4). Now, consider a patient with ARDS in this study admitted on March 14th who is destined to survive, and that he/she requires 21-25 days to recover. This patient will likely be discharged after April 4, excluding him/her from the analysis. Conversely, because death occurs earlier, a patient admitted at the same time who dies will be included in the analysis. This form of bias favoring inclusion of deaths while excluding survivors results in an overestimate of mortality.

How can we arrive at a more accurate estimate? We cannot assume all remaining inpatients currently alive will survive. Survival analysis techniques, such as the Kaplan-Meier method and Cox models can solve these problems. Why is proper methodology important? Respected public figures and journalists will quote the 88% mortality, and patients and their families will be frightened. The governor of New York has repeatedly cited a mortality greater than 80% amongst ventilated patients. However, that number is probably wrong. COVID-19 reported mortality in critically ill patients in other studies has varied, ranging from 22%-62%.5,6 The public will reasonably ask why is mortality excessively high in New York? Is quality of care poor? The answer is not that quality of care is poor, but rather that the data analysis is flawed.

On April 24 a clarification was published and the article was corrected (1). The clarification states:

In the Abstract, Results paragraph, the sentence reporting mortality for patients receiving mechanical ventilation should read, “As of April 4, 2020, for patients requiring mechanical ventilation (n = 1151, 20.2%), 38 (3.3%) were discharged alive, 282 (24.5%) died, and 831 (72.2%) remained in hospital.” This same sentence was added to the second paragraph of the Results section in the text. In the first paragraph of the text Results, the sentence about test results should read, “The first test for COVID-19 was positive in 5517 patients (96.8%), while 183 patients (3.2%) had a negative first test and positive repeat test.” In the Discussion, a paragraph was added to clarify the calculation of mortality rates. In Table 2, the number (%) of patients with concurrent entero/rhinovirus infection should be “22 (52.4).”

We hope this clarifies the findings.

REFERENCE

1. Clarification of Mortality Rate and Data in Abstract, Results, and Table 2. JAMA. Published online April 24, 2020. doi:10.1001/jama.2020.7681. Access at https://jamanetwork.com/journals/jama/fullarticle/2765367

We are grateful for the contribution Richardson and colleagues have made to the literature in difficult times [1]. In it, they describe the characteristics and co-morbidities of 5700 patients admitted to New York hospitals with COVID19.

Although they purport to describe outcomes for this same group of patients, only 2634 patients had been discharged or had died by the end of the study. The rest remained hospitalized, and their outcomes are yet to be determined. Nevertheless, the authors report the outcomes for the discharged patients as though they applied to the entire cohort. Particularly concerning was the conclusion that "Mortality for those requiring mechanical ventilation was 88.1%". This figure was widely reported in the mainstream press [2] and may be misinterpreted by some as evidence that mechanical ventilation in cases of COVID19 is generally futile. In fact, at the end of the study, of the 1151 ventilated patients, 38 patients had been discharged alive and 282 died. The majority of subjects (831) were still hospitalized.

When research subjects do not have an event during a period of observation, they are generally right-censored. Calculating mortality using only those subjects who have had an outcome by the end of the observation period offers a biased estimate of mortality, which Richardson and colleagues acknowledge as a limitation. If a time-to-event analysis is not performed, we think it would be more accurate to communicate the current mortality as approximately 24.5% (282/1151). While more of these patients will undoubtedly die, there is no reason to assume the hospital mortality will approach 90%. In fact, mortality may be no worse than other forms of ARDS, which carries in-hospital mortality of 40% [3]. This is an important distinction, because decision-making may well be different for conditions whose expected mortality is 1 in 4, or even 1 in 2, compared to 9 in 10.

We again thank Richardson and colleagues for sharing their data and experience, but we hope others will not be overly discouraged by their conclusions and avoid potentially life-saving ventilation based on these data.

This study, once again (Guan et al., 2020), points towards smokers as counterintuitively affected by COVID-19 in the same way (if not less) than non-smokers. It’s time to understand why the otherwise impaired lungs of smokers are not more sensitive to severe COVID-19 than non-smokers. The hypothesis of a decreased amount of ACE2 receptors is smokers is flebile, because SARS-Cov-2 enters pneumocytes also through other receptors (Milanetti et al., 2002; Iacobellis et al., 2020), and because old animals display a decrease in such receptors, compared with younger animals (Xie, 2006). We suggest to take bronchoalveolar lavage fluid from SARS-Cov-2-negative individuals and to test it with coronavirus-infected fluids, to understand whether smokers display a humoral protective factor that is able to decrease viral load.

There are clearly marked disparities between males and females. It would be useful to see data tabulated by sex across all fields for all reports such as this one.

CONFLICT OF INTEREST:None Reported

April 27, 2020

HIV and COVID-19

George Siberry, MD, MPH
| Public Health

The summary by Richardson and colleagues of COVID-19 patients hospitalized in NYC is instructive and appreciated. I note that there were 43 cases of COVID-19 that occurred in people living with HIV. The authors are implored to provide a summary of the presentations, characteristics and outcomes of this important subpopulation, as there remains very little published information about COVID-19 in the context of HIV infection.

Genomics-guided tracing of SARS-CoV-2 targets in human cells revealed very unusual, perhaps unique features of this virus. It highlighted discordant patterns of testosterone vs estradiol impacts on SARS-CoV-2 targets suggesting a plausible molecular explanation of the apparently higher male mortality during coronavirus pandemic (1). These findings are in agreement with several observations regarding discordant effects of androgens and estrogens described in the comments on this excellent clinical work. Of major concern is the ACE2 and FURIN expression in many human cells and tissues, including immune cells, suggesting that SARS-CoV-2 coronavirus may infect a broad range of cellular targets in the human body. Infection of immune cells may cause immunosuppression, long-term persistence of the virus, and spread of the virus to secondary targets. A similar path to the disease's broad impact is highlighted by the potential susceptibility to infection of endothelial cells denoted in comments. Clearly, testing must be expanded beyond individuals with classic symptoms of upper respiratory tract viral infection.

As a former data driven software application person I have an idea for an app that I think could change the game; I know, this is a big claim. Doctors who are interested in this data and also the development of apps to help society cope are the folks I want to reach, in hopes you might pass this idea around. This is a big claim but I think it can make a meaningful impact on how we as people come to grips with COVID-19. But it needs to be built and deployed.

I think laypeople have been troubled trying to understand and rationalize the fear we should or should not have related to COVID-19. What we hear and see is very confusing. Yet data are emerging, some in your study which I reference in the attachment, that with a bit of data science and software engineering will help us all.

Very simply my idea is for a mobile app that allows anyone to enter data about themselves and get a personalized understanding of the severity risk should they get the virus. It also would allow them to share this with other members of their family and friends so that they can assess the impact of being around each other.

A product brief is attached. I don't want anything for this idea although I would love to help in developing it. Feel free to share it far and wide if you know people who can help turn it into reality. this link will get you to my Product Brief:

Do you know how many of the 43 patients with HIV comorbidity got admitted to the ICU and how many cases were fatal? Also, what was the average length of stay?.

CONFLICT OF INTEREST:None Reported

April 30, 2020

Positive Disease Feedback by Reinhalation of Viral Particles

Richard Decker, PhD EE
| Emeritus Professor, Lehigh University

Is anyone currently considering the possibility of re-inhalation of (invisible) viral particles while patients are being mechanically ventilated? In advanced stages, the patient should be generating and exhaling large quantities of virus from lung cells that have reproduced the virus. Since these particles are prevented from exiting the ventilator (by filtration) to prevent contamination of the hospital environment, they have nowhere to go. If re-inhaled, they may increase the rate of progression of the disease. Is there any way to measure this effect, or is there a reason to discount the possibility of this mechanism of positive feedback?

CONFLICT OF INTEREST:None Reported

May 5, 2020

Risk Factors for COVID-19?

Muriel Gillick, MD
| Harvard Medical School

Richardson et al report that among 5700 consecutive patients with severe COVID-19 admitted to any of twelve hospitals in the greater New York City area, the most common co-morbidities were hypertension, obesity, and diabetes. To assess the importance of this observation, comparisons with rates in the population as a whole and age-adjusted rates would be very useful.

In the case of hypertension, the observed rate was 56.6 percent, very similar to the 60 percent rate reported among Americans over age 65 in the general population (1) and considerably higher than the rate of 32.2 percent found in the general population aged 40-59 (2).

In the case of diabetes, the observed rate of 33.8 percent among COVID-19 patients is similar to the rate of 27 percent found among the general population aged 65 and older, and markedly higher than the 17.5 percent found in the general population of 45-64-year-olds (3).

Finally, the 47.7 percent rate of obesity in the COVID-19 sample is comparable to the rate among adults generally: 44.8 percent of 40-59-year-olds and 42.8 percent of those over age 60 are obese (4).

Without knowing the age-specific rates of diabetes and hypertension in the COVID-19 patients, we cannot determine whether these diagnoses are risk factors and, if they are, to how great an extent. Consider three possibilities. Since the median age of the COVID-19 patients in the New York study is 63, roughly half are over age 65 and half under 65. This means that if the observed average rate of hypertension in the COVID-19 is the same for all age groups (one possibility), then the rate in the older age group is borderline lower than in the general elderly population (57 vs 60) and the rate in the younger patients is dramatically higher (57 vs 32). A second, even less plausible possibility is that the rate of hypertension in the older COVID-19 patients is lower than among their healthy counterparts, say 44 percent, making it a protective factor, and the rate in younger COVID-19 patients is very much higher than in their healthy counterparts, say 70 percent. A third and perhaps most likely possibility is that the observed average rate of hypertension in the COVID-19 patients reflects higher than typical rates in both young and old, say 44 percent in the young and 70 percent in the old. In only one of these scenarios is hypertension actually a risk factor for older patients and in that instance its contribution may be modest. The same argument can be made for diabetes. Obesity does not appear to be a risk factor at all since its prevalence in the COVID-19 population is the same as in the population at large, regardless of age.

Cautioning older individuals with hypertension or diabetes that they are at high risk and conversely, reassuring those without these conditions that they are at low risk is unwarranted without more convincing analysis.

George Niedt, MD, MBA
| Columbia University, Valegos College of Physicians and Surgeons

We are closing businesses and halting economic activity to “flatten the curve”. By cutting down on the number of infections we are lowering our need principally for respirators. According to the Harvard epidemiology website (1), in their worst case scenario (60% of Americans infected over a 6 month period), we are short 394,865 beds. If each of these beds is occupied at full capacity for the entire 6 month period, then 18 patients can be put in each bed over the 6 month period, or a total of 7,107,865 patients. However, the Harvard data does not account for how ineffective respirators have been in treating COVID19, as shown in this study. The survival of the patients on respirators is 11.8%, and only 2.7% for those over 65; thus in the best case scenario, we can save at most 838,728 lives. In this study the average age of death is approximately 74 years. The average number of years of life left at this age is 12 (US Gov. actuarial tables), therefore at best we can save 10,064,736 years of life by flattening the curve. As most of these folks have 2 or more comorbidities, we could estimate that the years of life saved are probably half of that, and quality years of life even less.

What are the costs associated with this? Well there are direct costs of approximately $40k for ICU care for each patient, regardless of whether they survive. Because only 11.8% of patients survive, that means a direct cost of $336,000 per patient. By closing the economy, we have already lost $50 trillion in market capital in the stock market. It is also estimated (by the Wall Street Journal) that we will lose approximately $1.5 trillion in lost GDP, and we have already authorized $2 trillion in federal relief, for a total of $3.5 trillion. This amounts to approximately $4,172,759 in indirect costs per life saved, a total cost of $4,508,759 per life, or a maximum of $375,729 per year of life saved, using an average life span, not the reduced lifespan of patients with comorbidities. This is an extraordinarily high number, but it doesn’t include the $50 trillion dollars of stock market losses. If we add these, then the numbers are more than 14 times as high. Normally, we regard $30K or less per year of life saved as a reasonable medical cost. The cost of COVID is 10 to 100 times that.
Things are worse for folks over 65. Only 2.7% of these patients survived mechanical ventilation: a direct cost of $1,480,000 per survivor. Seniors account for 56% of the ventilator use in this study. Using numbers analogous to those above, 3,980,404 beds would be used by seniors but only 107,471 senior lives would be saved by flattening the curve. If we apply 56% of GDP loss to just seniors, that amount to $18,273,478 per life saved (indirect) and 1,480,000 in direct costs, for a total of $19,717,478 per life or $2,075,524 per year of life saved, or likely twice that as these patients have comorbidities.

In sum, given the data, the cost of flattening the curve is high, and not cost effective vis a vis other medical treatments. Clearly, measures such as mask wearing, hand washing and social distancing without sheltering in place, would be much more cost effective, and not have ruined the economy. Sadly, much of this information was available in February, from data in Wuhan (2). We could have used it to make more rational decisions.

Richardson et al report numbers from the American epicenter and important data regarding known male gender vulnerability, besides hypertension, cardiovascular disease, obesity, and diabetes mellitus as frequent comorbidities and risk factors in patients with COVID-19 (1).

The male preponderance is alarming and needs further validation. According to their report, compared to females there is a prominent increased rates of COVID-19 and higher risk of severity and mortality in males in all adult age intervals; over 6 times more male fatalities in age range (40-49 years) and a general more male admissions.

Male risk raises many presumed explanations for sex vulnerability related to biological, molecular, immunological, and behavioral differences. Androgen-related expression of different genes and molecules is an example, such as Transmembrane Protease, Serine 2 (TMPRSS2), the protease that primes the Spike protein of the virus for infectivity (2).

Another explanation for male vulnerability for COVID-19 could be simply their tendency to grow facial hair; this sex-related feature of facial hair may interfere with masking adjustment and effectiveness. Neglected facial hair of admitted, and if sedated, COVID-19 patients might add to the risk of contamination of the main port-of-entry to the respiratory tract, the nose and mouth. Facial hair may become an auto-reservoir for repeated inoculation of contaminated droplets, adding to the viral load and its dose, resulting in further disease deterioration and worse outcome.

In their article (1), Dr. Richardson and colleagues reported that around 6% of cases had no underlying comorbidities and a similar proportion of their cohort (5700 cases) had only one risk factor for COVID-19. It would be highly informative to elaborate on their gender and on the issue of "neglected" facial hair. Presumably, these patients with one or no risk factors of comorbidities, are mainly bearded men. This missing information in this important article (1) may assist to solve this debate; is male sex risk behavioral or inherent?

My subtle clinical viewpoint from observations of young patients with severe COVID-19, lacking any underlying sickness and of those from Israeli communities, where facial hair and even braids are common due to religious and traditional reasons, could be supported by this missing information, and might shed light on a proposed assumption that "hairborne" transmission is an epidemiological essential issue in COVID-19 pandemic, and should be addressed.

I assume that all patients who received mechanical ventilation (MV) were in ICU. I also assume that all patients admitted to ICU were quite homogeneously ill, namely critically ill.

Then, a reasonable and easily available subset analysis would be to split all ICU patients into those who received MV and did not receive MV. For these subsets I extracted the following outcome categories from Table 5 of the article:

As the previous comment states, the d-dimer question is crucial as it may shed light on whether treating early for coagulopathy could help reduce or avoid mechanical ventilation (1). Pulmonary Intravascular Coagulopathy (PIC) has also been proposed as the pathophysiology of CoVID 19 (2). Both endothelial dysfunction and PIC could explain the coagulopathy seen in CoVID 19, but treating the clotting cascade early with anticoagulants like LMW Heparin may be beneficial as has been reported in a preprint retrospective study (3).

References

1. Oudkerk M et.al. Diagnosis, Prevention, and Treatment of Thromboembolic Complications in COVID-19: Report of the National Institute for Public Health of the Netherlands. Radiology Published Online Apr 23 2020. https://doi.org/10.1148/radiol.2020201629

QuestionWhat are the characteristics, clinical presentation, and outcomes of patients hospitalized with coronavirus disease 2019 (COVID-19) in the US?

FindingsIn this case series that included 5700 patients hospitalized with COVID-19 in the New York City area, the most common comorbidities were hypertension, obesity, and diabetes. Among patients who were discharged or died (n = 2634), 14.2% were treated in the intensive care unit, 12.2% received invasive mechanical ventilation, 3.2% were treated with kidney replacement therapy, and 21% died.

MeaningThis study provides characteristics and early outcomes of patients hospitalized with COVID-19 in the New York City area.

Abstract

ImportanceThere is limited information describing the presenting characteristics and outcomes of US patients requiring hospitalization for coronavirus disease 2019 (COVID-19).

ObjectiveTo describe the clinical characteristics and outcomes of patients with COVID-19 hospitalized in a US health care system.

Design, Setting, and ParticipantsCase series of patients with COVID-19 admitted to 12 hospitals in New York City, Long Island, and Westchester County, New York, within the Northwell Health system. The study included all sequentially hospitalized patients between March 1, 2020, and April 4, 2020, inclusive of these dates.

Main Outcomes and MeasuresClinical outcomes during hospitalization, such as invasive mechanical ventilation, kidney replacement therapy, and death. Demographics, baseline comorbidities, presenting vital signs, and test results were also collected.

ResultsA total of 5700 patients were included (median age, 63 years [interquartile range {IQR}, 52-75; range, 0-107 years]; 39.7% female). The most common comorbidities were hypertension (3026; 56.6%), obesity (1737; 41.7%), and diabetes (1808; 33.8%). At triage, 30.7% of patients were febrile, 17.3% had a respiratory rate greater than 24 breaths/min, and 27.8% received supplemental oxygen. The rate of respiratory virus co-infection was 2.1%. Outcomes were assessed for 2634 patients who were discharged or had died at the study end point. During hospitalization, 373 patients (14.2%) (median age, 68 years [IQR, 56-78]; 33.5% female) were treated in the intensive care unit care, 320 (12.2%) received invasive mechanical ventilation, 81 (3.2%) were treated with kidney replacement therapy, and 553 (21%) died. As of April 4, 2020, for patients requiring mechanical ventilation (n = 1151, 20.2%), 38 (3.3%) were discharged alive, 282 (24.5%) died, and 831 (72.2%) remained in hospital. The median postdischarge follow-up time was 4.4 days (IQR, 2.2-9.3). A total of 45 patients (2.2%) were readmitted during the study period. The median time to readmission was 3 days (IQR, 1.0-4.5) for readmitted patients. Among the 3066 patients who remained hospitalized at the final study follow-up date (median age, 65 years [IQR, 54-75]), the median follow-up at time of censoring was 4.5 days (IQR, 2.4-8.1).

Conclusions and RelevanceThis case series provides characteristics and early outcomes of sequentially hospitalized patients with confirmed COVID-19 in the New York City area.

Introduction

The first confirmed case of coronavirus disease 2019 (COVID-19) in the US was reported from Washington State on January 31, 2020.1 Soon after, Washington and California reported outbreaks, and cases in the US have now exceeded total cases reported in both Italy and China.2 The rate of infections in New York, with its high population density, has exceeded every other state, and, as of April 20, 2020, it has more than 30% of all of the US cases.3

Limited information has been available to describe the presenting characteristics and outcomes of US patients requiring hospitalization with this illness. In a retrospective cohort study from China, hospitalized patients were predominantly men with a median age of 56 years; 26% required intensive care unit (ICU) care, and there was a 28% mortality rate.4 However, there are significant differences between China and the US in population demographics,5 smoking rates,6 and prevalence of comorbidities.7

This study describes the demographics, baseline comorbidities, presenting clinical tests, and outcomes of the first sequentially hospitalized patients with COVID-19 from an academic health care system in New York.

Methods

The study was conducted at hospitals in Northwell Health, the largest academic health system in New York, serving approximately 11 million persons in Long Island, Westchester County, and New York City. The Northwell Health institutional review board approved this case series as minimal-risk research using data collected for routine clinical practice and waived the requirement for informed consent. All consecutive patients who were sufficiently medically ill to require hospital admission with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection by positive result on polymerase chain reaction testing of a nasopharyngeal sample were included. Patients were admitted to any of 12 Northwell Health acute care hospitals between March 1, 2020, and April 4, 2020, inclusive of those dates. Clinical outcomes were monitored until April 4, 2020, the final date of follow-up.

Data were collected from the enterprise electronic health record (Sunrise Clinical Manager; Allscripts) reporting database, and all analyses were performed using version 3.5.2 of the R programming language (R Project for Statistical Computing; R Foundation). Patients were considered to have confirmed infection if the initial test result was positive or if it was negative but repeat testing was positive. Repeat tests were performed on inpatients during hospitalization shortly after initial test results were available if there was a high clinical pretest probability of COVID-19 or if the initial negative test result had been judged likely to be a false-negative due to poor sample collection. Transfers from one in-system hospital to another were merged and considered as a single visit. There were no transfers into or out of the system. For patients with a readmission during the study period, data from the first admission are presented.

Data collected included patient demographic information, comorbidities, home medications, triage vitals, initial laboratory tests, initial electrocardiogram results, diagnoses during the hospital course, inpatient medications, treatments (including invasive mechanical ventilation and kidney replacement therapy), and outcomes (including length of stay, discharge, readmission, and mortality). Demographics, baseline comorbidities, and presenting clinical studies were available for all admitted patients. All clinical outcomes are presented for patients who completed their hospital course at study end (discharged alive or dead). Clinical outcomes available for those in hospital at the study end point are presented, including invasive mechanical ventilation, ICU care, kidney replacement therapy, and length of stay in hospital. Outcomes such as discharge disposition and readmission were not available for patients in hospital at study end because they had not completed their hospital course. Home medications were reported based on the admission medication reconciliation by the inpatient-accepting physician because this is the most reliable record of home medications. Final reconciliation has been delayed until discharge during the current pandemic. Home medications are therefore presented only for patients who have completed their hospital course to ensure accuracy.

Race and ethnicity data were collected by self-report in prespecified fixed categories. These data were included as study variables to characterize admitted patients. Initial laboratory testing was defined as the first test results available, typically within 24 hours of admission. For initial laboratory testing and clinical studies for which not all patients had values, percentages of total patients with completed tests are shown. The Charlson Comorbidity Index predicts 10-year survival in patients with multiple comorbidities and was used as a measure of total comorbidity burden.8 The lowest score of 0 corresponds to a 98% estimated 10-year survival rate. Increasing age in decades older than age 50 years and comorbidities, including congestive heart disease and cancer, increase the total score and decrease the estimated 10-year survival. A total of 16 comorbidities are included. A score of 7 points and above corresponds to a 0% estimated 10-year survival rate. Acute kidney injury was identified as an increase in serum creatinine by 0.3 mg/dL or more (≥26.5 μmol/L) within 48 hours or an increase in serum creatinine to 1.5 times or more baseline within the prior 7 days compared with the preceding 1 year of data in acute care medical records. This was based on the Kidney Disease: Improving Global Outcomes (KDIGO) definition.9 Acute hepatic injury was defined as an elevation in aspartate aminotransferase or alanine aminotransferase of more than 15 times the upper limit of normal.

Results

A total of 5700 patients were included (median age, 63 years [interquartile range {IQR}, 52-75; range, 0-107 years]; 39.7% female) (Table 1). The median time to obtain polymerase chain reaction testing results was 15.4 hours (IQR, 7.8-24.3). The most common comorbidities were hypertension (3026, 56.6%), obesity (1737, 41.7%), and diabetes (1808, 33.8%). The median score on the Charlson Comorbidity Index was 4 points (IQR, 2-6), which corresponds to a 53% estimated 10-year survival and reflects a significant comorbidity burden for these patients. At triage, 1734 patients (30.7%) were febrile, 986 (17.3%) had a respiratory rate greater than 24 breaths/min, and 1584 (27.8%) received supplemental oxygen (Table 2 and Table 3). The first test for COVID-19 was positive in 5517 patients (96.8%), while 183 patients (3.2%) had a negative first test and positive repeat test. The rate of co-infection with another respiratory virus for those tested was 2.1% (42/1996). Discharge disposition by 10-year age intervals of all 5700 study patients is included in Table 4. Length of stay for those who died, were discharged alive, and remained in hospital are presented as well. Among the 3066 patients who remained hospitalized at the final study follow-up date (median age, 65 years [IQR 54-75]), the median follow-up at time of censoring was 4.5 days (IQR, 2.4-8.1). Mortality was 0% (0/20) for male and female patients younger than 20 years. Mortality rates were higher for male compared with female patients at every 10-year age interval older than 20 years.

Outcomes for Patients Who Were Discharged or Died

Among the 2634 patients who were discharged or had died at the study end point, during hospitalization, 373 (14.2%) were treated in the ICU, 320 (12.2%) received invasive mechanical ventilation, 81 (3.2%) were treated with kidney replacement therapy, and 553 (21%) died (Table 5). As of April 4, 2020, for patients requiring mechanical ventilation (n = 1151, 20.2%), 38 (3.3%) were discharged alive, 282 (24.5%) died, and 831 (72.2%) remained in hospital. Mortality rates for those who received mechanical ventilation in the 18-to-65 and older-than-65 age groups were 76.4% and 97.2%, respectively. Mortality rates for those in the 18-to-65 and older-than-65 age groups who did not receive mechanical ventilation were 1.98% and 26.6%, respectively. There were no deaths in the younger-than-18 age group. The overall length of stay was 4.1 days (IQR, 2.3-6.8). The median postdischarge follow-up time was 4.4 days (IQR, 2.2-9.3). A total of 45 patients (2.2%) were readmitted during the study period. The median time to readmission was 3 days (IQR, 1.0-4.5). Of the patients who were discharged or had died at the study end point, 436 (16.6%) were younger than age 50 with a score of 0 on the Charlson Comorbidity Index, of whom 9 died.

Outcomes by Age and Risk Factors

For both patients discharged alive and those who died, the percentage of patients who were treated in the ICU or received invasive mechanical ventilation was increased for the 18-to-65 age group compared with the older-than-65 years age group (Table 5). For patients discharged alive, the lowest absolute lymphocyte count during hospital course was lower for progressively older age groups. For patients discharged alive, the readmission rates and the percentage of patients discharged to a facility (such as a nursing home or rehabilitation), as opposed to home, increased for progressively older age groups.

Of the patients who died, those with diabetes were more likely to have received invasive mechanical ventilation or care in the ICU compared with those who did not have diabetes (eTable 1 in the Supplement). Of the patients who died, those with hypertension were less likely to have received invasive mechanical ventilation or care in the ICU compared with those without hypertension. The percentage of patients who developed acute kidney injury was increased in the subgroups with diabetes compared with subgroups without those conditions.

Home medication reconciliation information was available for 2411 (92%) of the 2634 patients who were discharged or who died by the study end. Of these 2411 patients, 189 (7.8%) were taking an angiotensin-converting enzyme inhibitor (ACEi) at home and 267 (11.1%) were taking an angiotensin II receptor blocker (ARB) at home. The median number of total home medications was 3 (IQR, 0-7). Outcomes for subgroups of patients with hypertension by use of ACEi or ARB home medication are shown in eTable 2 in the Supplement. Numbers provided for total patients taking ACEi or ARB therapy in eTable 2 in the Supplement are provided only for patients who also had a diagnosis of hypertension.

Of the patients taking an ACEi at home, 91 (48.1%) continued taking an ACEi while in the hospital and the remainder discontinued this type of medication during their hospital visit. Of the patients taking an ARB at home, 136 (50.1%) continued taking an ARB while in the hospital and the remainder discontinued taking this type of medication during their hospital visit. Of patients who were not prescribed an ACEi or ARB at home, 49 started treatment with an ACEi and 58 started treatment with an ARB during their hospitalization. Mortality rates for patients with hypertension not taking an ACEi or ARB, taking an ACEi, and taking an ARB were 26.7%, 32.7%, and 30.6%, respectively.

Discussion

To our knowledge, this study represents the first large case series of sequentially hospitalized patients with confirmed COVID-19 in the US. Older persons, men, and those with pre-existing hypertension and/or diabetes were highly prevalent in this case series and the pattern was similar to data reported from China.4 However, mortality rates in this case series were significantly lower, possibly due to differences in thresholds for hospitalization. This study reported mortality rates only for patients with definite outcomes (discharge or death), and longer-term study may find different mortality rates as different segments of the population are infected. The findings of high mortality rates among ventilated patients are similar to smaller case series reports of critically ill patients in the US.10

ACEi and ARB medications can significantly increase mRNA expression of cardiac angiotensin-converting enzyme 2 (ACE2),11 leading to speculation about the possible adverse, protective, or biphasic effects of treatment with these medications.12 This is an important concern because these medications are the most prevalent antihypertensive medications among all drug classes.13 However, this case series design cannot address the complexity of this question, and the results are unadjusted for known confounders, including age, sex, race, ethnicity, socioeconomic status indicators, and comorbidities such as diabetes, chronic kidney disease, and heart failure.

Mortality rates are calculated only for patients who were discharged alive or died by the study end point. This biases our rates toward including more patients who died early in their hospital course. Most patients in this study were still in hospital at the study end point (3066, 53.8%). We expect that as these patients complete their hospital course, reported mortality rates will decline.

Limitations

This study has several limitations. First, the study population only included patients within the New York metropolitan area. Second, the data were collected from the electronic health record database. This precluded the level of detail possible with a manual medical record review. Third, the median postdischarge follow-up time was relatively brief at 4.4 days (IQR, 2.2-9.3). Fourth, subgroup descriptive statistics were unadjusted for potential confounders. Fifth, clinical outcome data were available for only 46.2% of admitted patients. The absence of data on patients who remained hospitalized at the final study date may have biased the findings, including the high mortality rate of patients who received mechanical ventilation older than age 65 years.

Conclusions

This case series provides characteristics and early outcomes of sequentially hospitalized patients with confirmed COVID-19 in the New York City area.

Correction: This article was corrected on April 24, 2020, to clarify the mortality rate of ventilated patients, correct the COVID-19 positive/negative test results, and correct the data for concurrent entero/rhinovirus infection in Table 2.

Conflict of Interest Disclosures: Dr Crawford reported receiving grants from Regeneron outside the submitted work. Dr Becker reported serving on the scientific advisory board for Nihon Kohden and receiving grants from the National Institutes of Health, United Therapeutics, Philips, Zoll, and Patient-Centered Outcomes Research Institute outside the submitted work. Dr Cohen reported receiving personal fees from Infervision outside the submitted work. No other disclosures were reported.

Funding/Support: This work was supported by grants R24AG064191 from the National Institute on Aging of the National Institutes of Health; R01LM012836 from the National Library of Medicine of the National Institutes of Health; and K23HL145114 from the National Heart, Lung, and Blood Institute.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The views expressed in this article are those of the authors and do not represent the views of the National Institutes of Health, the US Department of Health and Human Services, or any other government entity. Karina W. Davidson is a member of the US Preventive Services Task Force (USPSTF). This article does not represent the views and policies of the USPSTF.